April 20, 2017

Download Advances in Neural Networks - ISNN 2010: 7th International by Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, PDF

By Longwen Huang, Si Wu (auth.), Liqing Zhang, Bao-Liang Lu, James Kwok (eds.)

This ebook and its sister quantity acquire refereed papers provided on the seventh Inter- tional Symposium on Neural Networks (ISNN 2010), held in Shanghai, China, June 6-9, 2010. development at the luck of the former six successive ISNN symposiums, ISNN has turn into a well-established sequence of renowned and top quality meetings on neural computation and its purposes. ISNN goals at delivering a platform for scientists, researchers, engineers, in addition to scholars to assemble jointly to offer and talk about the most recent progresses in neural networks, and functions in various components. these days, the sector of neural networks has been fostered a long way past the normal man made neural networks. This yr, ISNN 2010 got 591 submissions from greater than forty nations and areas. in keeping with rigorous experiences, one hundred seventy papers have been chosen for booklet within the complaints. The papers accrued within the lawsuits conceal a vast spectrum of fields, starting from neurophysiological experiments, neural modeling to extensions and functions of neural networks. we now have equipped the papers into volumes according to their themes. the 1st quantity, entitled “Advances in Neural Networks- ISNN 2010, half 1,” covers the subsequent issues: neurophysiological starting place, conception and versions, studying and inference, neurodynamics. the second one quantity en- tled “Advance in Neural Networks ISNN 2010, half 2” covers the next 5 subject matters: SVM and kernel equipment, imaginative and prescient and photo, facts mining and textual content research, BCI and mind imaging, and applications.

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Additional resources for Advances in Neural Networks - ISNN 2010: 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I

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Amel Grissa Touzi 625 Author Index . . . . . . . . . . . . . . . . . . . . . . . . . 637 Stimulus-Dependent Noise Facilitates Tracking Performances of Neuronal Networks Longwen Huang1 and Si Wu2 1 2 Yuanpei Program and Center for Theoretical Biology, Peking University, Beijing, China Lab of Neural Information Processing, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai, China Abstract. Understanding why neural systems can process information extremely fast is a fundamental question in theoretical neuroscience.

In addition, the robustness of period against change in range parameter occurs. The range parameter play an important role in dynamical behaviors of neural network model (2) with delayed dependent parameters. We can control the dynamical behaviors of the model (2) by modulating the range parameter. The method proposed in this paper is important for understanding the regulatory mechanisms of neural network. Moreover, the method provides a control mechanism to ensure a transition from an equilibrium to a periodic oscillation with a desired and robust amplitude and period.

So, what kind of noise structure is most suitable for fast neural computation, in particular, for the tracking task we consider? In the above analysis, for two network models, we have found that when the input noise is Poissonian, the network transient dynamics has two important properties, which are: 1) the mean of external input is linearly encoded by the instant firing rate of the network when the network is at a stationary state; and 2) the stationary state of the network is insensitive to the change of the stimulus value (the mean of external input).

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